YX-IDCRC: a general approach for calculating interdiffusion coefficients from raw experimental data and its application
Bibliographic record
Abstract
The Boltzmann-Matano method and its derivatives are widely used approaches for calculating interdiffusion coefficients. However, raw practical C-x curves typically have to be modified to fit with various functions due to their fluctuations, failing to reflect their essential characteristics. Herein, we introduce YX-IDCRC and YX-IDCRC software to calculate the interdiffusion coefficient using raw C-x curves. Firstly, we apply YX-IDCRC for the ideal (not fluctuate) and simulated (with noises) data, the calculated average of the ideal data closely matches the reported result. Secondly, we propose a method to determine the FWHM interval and distinguish the abnormal points by examining the calculated. Thirdly, we prepare the Cr2O3-CaO diffusion couples in the Fe2O3 matrix, and the average at 1273, 1298, 1323, and 1348 K are calculated as 3.10×10−9, 4.82×10−9, 7.02×10−9, and 2.4×10−8 cm2·s−1, respectively. Deduction from the Arrhenius equation indicates the diffusion activation energy (ED) as 369.97±51.80 kJ·mol−1, which lies in the reported results and verifies the reliability of YX-IDCRC on the practical diffusion systems. Finally, the supervision of limitation and applicability indicates that YX-IDCRC is applicable for the binary or pseudo-binary systems with S- or half-S-shape C-x curves.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.009 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".